Hyperspectral image dimension reduction system and method

ABSTRACT

Provided is a method of hyperspectral image dimension reduction. The method includes receiving a hyperspectral image having a plurality of pixels. A set of basis vectors is established at least in part with respect to the spectral vectors of the initial hyperspectral image. For each pixel of the hyperspectral image, the spectral vector is read and decomposed, i.e. unmixed, with the basis vector set to provide at least a reduced dimension vector for each pixel. Collectively the reduced dimension vectors for each pixel represent the dimensionally reduced image. A system operable to perform the method is also provided.

FIELD

This invention relates generally to the field of hyperspectral imageprocessing, and in particular to a system and method for hyperspectralimage dimension reduction such as may be desired for data transmissionor simplified processing.

BACKGROUND

A black and white photograph of an object or a geographic area is a twodimensional construct of the actual image or area—for each X and Ycoordinate in the image there is a single value blackness or whitenessof that particular image spot. As human beings, the eye can perceiveuseful information about objects or areas based on the differencesbetween black, white, and the shades of gray.

Color photographs add more visual information, but for most purposes thecolor information that is represented is tied to the visual spectrum.For each X and Y coordinate in the image there is an approximation ofthe visual color spectrum of that particular image spot created throughthe blending of three color values, such as for example Red Green andBlue.

Multispectral sensing systems such as the Landsat Thematic Mapper remoteimager and weather satellites produce images with a few relatively broadwavelength bands. The imager may capture a visual spectrum image andalso one in infrared, but still they are limited in their ability toperceive information that may otherwise be present in a different partof the spectrum.

Hyperspectral sensors, on the other hand, collect image data acrossdozens if not hundreds of spectral bands, combining the technology ofspectroscopy and remote imaging. The measurements captured byhyperspectral sensors make it possible to derive a contiguous spectrumfor each image pixel. In other words for each X and Y coordinate of apixel, rather than a single value for a gray or visible color, there isa third dimension—a vector, providing distinct information for thatparticular pixel across the large spectrum of wavelengths.

As different materials will reflect wavelengths of visible and invisiblelight selectively, analysis of the contiguous spectrum permits finerresolution and greater perception of information contained in the image,by separating and evaluating different wavelengths. For example,inorganic materials such as minerals, chemical compositions andcrystalline structures control the shape of the spectral curve and thepresence and positions of specific absorption bands.

The spectral reflectance curves of healthy green plants also have acharacteristic shape that is dictated by various plant attributes, suchas the absorption effects from chlorophyll and other leaf pigments. Leafstructure varies significantly between plant species, and can beaffected by plant stress. Therefore species type, plant stress andcanopy state can all affect near infrared reflectance measurements,which are captured by hyperspectral sensors.

In addition, for a given pixel, a combination of different materials,e.g., biological, chemical, mineral, will provide a composite signal.Upon analysis, and through comparison to known signal waveforms it isfrequently possible to derive the presence of materials within a pixel,and therefore appreciate a detection granularity that is greater thanthe actual pixel resolution.

Hyperspectral sensors providing hyperspectral imaging can therefore bebeneficially applied in a wide array of practical applications. Examplesof such uses include aid in the detection of chemical or biologicalweapons, bomb damage assessment of underground structures, drugproduction and cultivation, as well as the detection of friend or foetroops and vehicles beneath foliage or camouflage.

With each pixel having information for a spectrum of wavelengths, it isnot surprising that hyperspectral images are represented by vastquantities of data. As the hyperspectral sensors are often locatedremotely, such as in satellites or in aircraft, the transmission of thedata can be an issue as the volume of data can be enormous andtransmission of such data to ground stations typically involve a radiotransmission having a limited frequency bandwidth.

More specifically, the technical increases in hyperspectral sensors tocapture images with greater spatial content, increased resolution,and/or across greater wavelength spectrums has raised the total datavolume to the point where data latency is a problem and a limitingfactor in the distribution of near real time information. When theimaged area corresponds to a disaster area, such as coast lines ravagedby a tsunami, a combat zone, or a terrorist strike upon a target, realtime transfer and processing is key to the prevention of loss of lifeand property.

In addition, for some applications there is great advantage to the databeing processed at the location where the data is collected and for theprocessing results and/or selected subsets of the data to be transmittedto users. Given the size of the data set corresponding to ahyperspectral image, in many instances localized processing maysignificantly tax localized resources and/or lead to an even greaterdelay in transmitting the data to a user.

One option to increase transmission rate is to increase the usablecommunications bandwidth. Such an increase typically requires additionalpower at the transmission source. For existing satellites and evenfuture satellites where power sources are limited, increasing bandwidthis not ideal. Another option to increase transmission rate is toactually reduce the volume of data that is being transmitted. A commonmeasure for such reduction is compression, which may be performed in oneof two forms—lossless and lossy.

A related function is data dimensionality reduction (DIMRED). DIMRED issimilar to compression in that the volume of data is reduced. It differsfrom compression in that the intent of DIMRED is to enable furtherprocessing of the data (e.g. to detect targets, to measure changes insuccessive images of the same scene) without decompressing the data set.As with compression, however, contemporary methods for data dimensionreduction suffer drawbacks that are often quite undesirable.

Typically, prior art methods DIMRED methods include forming a data setof many fewer spectral bands by merging or selecting a subset ofspectral bands of a hyperspectral data cube or the use of principalcomponents analysis (PCA) to create basis vectors. In the latter casethe eigenvectors and corresponding eigenvalues of the hyperspectral dataset are computed. B basis vectors are selected by choosing the Beigenvectors with the B largest eigenvalues. Other techniques have beenemployed to reduce scene dimension based on linear unmixing.

It is well known that most hyperspectral images can be well representedby the linear superposition of the spectra of the mixture of materialsfound in each spatial pixel. As such, if the spectra for those materialsare known then a linear algebra technique known as unmixing can beapplied to find the abundance of each spectrum in each pixel. Theabundance or unmixing coefficients can then be used to represent theinformation content of each pixel.

However, and quite importantly, most of the time the constituent spectraof the scene are not known a priori. Some prior art algorithms attemptto compute these pure material spectra from the spectra of mixed pixelsin the scene. Other prior art has found mathematical basis functionsthat can be superimposed to represent each pixel. Examples of suchmethods include: the Orassis algorithm that fits an optimal convex hullaround the high dimensionality data trying to find the endmembers orvertices of the hull; the Nfindr technique by Winter attempts to findthe spectrally purest pixels and make them endmembers; and the Max Dtechnique from RIT which is similar to NFINDR. The commercial tool ENVIoffers various versions of the Principal Components Analysis (PCA).

The pixel purity and endmember techniques have degraded or limitedperformance when pure pixels are not found in the scene and offer lessefficient reduction/compaction than Principal Components and the presentinvention. They also do not control either the average pixel error orthe worst case pixel error; the latter being very important ifsubsequent processing is going to detect anomalies.

The Residual correlation method from BAE applies endmember selectioncriteria that are subjective and rely on lab or a priori spectra. ENVIand other processing tools offer analysts the option to select pixelsfrom a scene manually to be used as endmembers. Analyst selections aresubjective and do not control the errors in a predictable or optimalfashion.

An overview of many of these techniques can be found in the followingreferences. Winter, Michael E., “N-FINDR: an Algorithm for FastAutonomous Spectral End-member Determination in Hyperspectral Data”,Proc of SPIE Vol 3753, Imaging Spectrometry V (Descour and Shen editors)pp 266-277, 1999; Keshava, Nirmal, A Survey of Spectral UnmixingAlgorithms, LINCOLN LABORATORY JOURNAL VOLUME 14, NUMBER 1, 2003;Robinson Ian S. et al., “Target Detection Using HSI Systems: An UpdateOf TRW Results”, in Imaging Spectrometry IV, S Shen and M R Descour,Eds., SPIE Vol 3438, San Diego, Calif., July 1998; and McGregor, Dan. etal., Comparison of Hyperspectral Sub-Pixel Detection With and Without APriori Knowledge of Target Features, 1998 IEEE Aerospace Conf. Proc.,Vol. 5, Snow Mass Colo., Mar. 1998.

Moreover, PCA analysis is generally effective in creating basis vectorsthat span the scene in a mean-square sense. However, PCA does notdirectly control the error levels of fidelity of individual spectra.Thus it is possible, and even likely, to incur a large error in a smallfraction of the scene, especially when and where the pixels areuncommon. This error can be extremely detrimental when the detectionproblem is for spectrally unique objects that sparsely populate thescene. In addition, PCA analysis typically requires high overheadincluding computation and inversion of the spectral covariance matrix.

Lossless data compression is a class of data compression algorithms thatpermits the extraction of the original data from the compressed data tobe complete, i.e., the restored data is exactly the same as the originaldata. Lossless algorithms typically are categorized according to thetype of data that they are designed to compress, such as text, imagesand sound. Zip and gzip file formats are common lossless applicationsfor data, and PNG and GIF are frequently used for images.

While in principle, any general-purpose lossless compression algorithmcan be used on any type of data, many lossless algorithms are unable toachieve significant compression, especially on data that is not of theform they were designed to compress. There is no guaranteed compressionfactor for lossless compression. In certain cases the output from such alossless algorithm may actually be larger then the original.

Lossy data compression is a class of data compression algorithms wherecompressing data and then decompressing provides a new data stream thatmay well be different from the original, but close enough to be usefulfor one or more applications. Lossy methods are frequently employedrather than lossless methods because the compressed file size is farsmaller while still meeting the requirements of the application.

Common examples of lossy compressed data files are JPEG image files andMP3 audio files. JPEG is designed specifically to provide images thatrespond well to the human eye. With JPEG, the image is converted fromRGB (Red Green Blue) into the YCbCr color space, wherein Y representsthe brightness of a pixel, the Cb and Cr respectively representchrominance as split into blue and red components.

As the human eye sees more detail in the Y component of brightness thenin the Cb (blue) and Cr (red), JPEG downsamples the Cb and Cr elements.JPEG also performs a normalized, two-dimensional type II discrete cosinetransform to further reduce the data. MP3 compression likewise removesdata that is not likely to be perceived by the human user. However, theremoval of data in both JPEG and MP3 compression methods does result innew files that although acceptable to human users may not be acceptablefor computer processing and analysis. More specifically, JPEG, MP3 andother lossy compression methods may well remove information that isvaluable and desired, but potentially not realized to be so untilprocessing and analysis is attempted.

As hyperspectral image data is quite large due to the wavelengthspectrum captured, most lossless compression methods have not proven tobe truly effective in balancing compression desirability whilemaintaining important data. This is especially true where a factorinvolved in the compression method is unrelated to the data of the imageitself, and/or where the method is highly dependent on statisticalrepetition of the data and the ability to represent a statistical groupof pixels with a token representative.

As indicated above, with hyperspectral images it is very often possibleto infer information from a single pixel so as to determine the presenceof materials within the area of a pixel, e.g., a camouflaged tank ormissile below a canopy of foliage, even when the detected element issmaller than the resolution of the pixel. When a compression systemremoves data from the pixel, this desirability for material detectionmay be inadvertently hampered.

Hence there is a need for an image dimension reduction system and methodsuitable for use with hyperspectral images that overcome one or more oftechnical problems noted above.

SUMMARY

This invention provides a system and method for hyperspectral imagedimension reduction.

In particular, and by way of example only, according to one embodimentof the present invention, provided is a method of hyperspectral imagedimension reduction, including: receiving a hyperspectral image having aplurality of pixels and establishing a basis vector (BV) set. For eachof the plurality of pixels, the spectral vector is read from the pixel.The spectral vector is then decomposed with the BV set to provide areduced dimension vector for the pixel. The provided output is adimension reduced image consisting of the reduced data vectors for eachpixel.

In at least one alternative embodiment, provided is a method ofhyperspectral image dimension reduction, including: receiving ahyperspectral image having a plurality of pixels and establishing abasis vector (BV) set. The BV set is established by selecting an initialpixel and reading the spectral vector for the initial pixel as aninitial basis vector (BV) of the set. For each of the remainingplurality of pixels taken in turn, each spectral vector is read andunmixed with the BV set to determine a residual vector. In response tothe magnitude of the residual vector being greater than a threshold,adding the spectral vector to the BV set. With the BV set soestablished, then returning to read the spectral vector from the pixelonce again. In this pass the spectral vector is unmixed with the BV setto provide an alpha vector and a residual vector for the pixel. Theprovided output is a dimension reduced image consisting of at least thealpha vector for each pixel.

In yet another embodiment, provided is a system for hyperspectral imagedimension reduction. The system includes an interface operable toreceive at least a hyperspectral image having a plurality of pixels; aspectrum reader operable to read a spectral vector for each pixel; apool operable to provide a basis vector (BV) set having at least onebasis vector; a decomposer operable to decompose the spectral vector ofeach pixel with at least one basis vector and provide a result; and anevaluator in connection with the pool and decomposer and operable in atleast a first instance to compare the result to a threshold value, andoperable in at least a second instance to provide an alpha vector and aresidual vector for each pixel.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a simplified diagram of a hyperspectral imagingsystem including an embodiment of the hyperspectral image dimensionreduction system of the present invention;

FIG. 2 is an exemplary hyperspectral image as may be dimensionallyreduced in accordance with at least one embodiment of the presentinvention;

FIG. 3 is block diagram of a hyperspectral image dimension reductionsystem in accordance with at least one embodiment of the presentinvention;

FIG. 4 is a high level block diagram presenting at least one method ofhyperspectral image dimension reduction in accordance with at least oneembodiment of the present invention;

FIG. 5 is a more specific block diagram of a method of hyperspectralimage dimension reduction in accordance with at least one embodiment ofthe present invention; and

FIG. 6 is a block diagram of a computer system in accordance with one ormore embodiments.

DETAILED DESCRIPTION

Before proceeding with the detailed description, it is to be appreciatedthat the present teaching is by way of example only, not by limitation.The concepts herein are not limited to use or application with aspecific type of problem solving system. Thus, although theinstrumentalities described herein are for the convenience ofexplanation, shown and described with respect to exemplary embodiments,it will be appreciated that the principles herein may be applied equallyin other types of image dimension reduction systems and method.

FIG. 1 illustrates an exemplary image dimension reduction system(“IDRS”) 100 incorporated as part of a hyperspectral image system 102provided by a satellite 104. The satellite 104 is tasked to image ageographical area 106, and in so doing receives light 108 from a singleground-resolution cell. To generalize the basic elements provided by ahyperspectral image system 102 and the general method of operation, thelight 108 is received by scan mirrors 110 and or other optics thatdirect the light through at least one dispersing element 112.

The dispersing element separates wavelengths and provides them toimaging optics 114 which in turn focus the wavelengths upon an array ofdetectors 116 arranged to capture data information across a spectrum ofwavelengths. The hyperspectral data is then processed by IDRS 100 fortransmission 118 to a ground station 120.

As is further described below, the degree of dimension reduction andclassification as lossy or lossless is an adjustable feature of IDRS100. Further, the transmitted dimension reduced data 118 is suitable forimmediate processing without requiring reconstruction of the originaldata, or an approximation of the original data.

FIG. 2 is a further representation of the image 200, and specificallyimages? 200 i˜n, acquired simultaneously in many different adjacentwavelength bands. As indicated, the image is comprised of pixelsarranged in an X-Y coordinate system. Under appropriate circumstances,alternative coordinate systems may be employed, however X-Y isrepresented herein for ease of illustration and discussion, and not byway of limitation. Each pixel 202 has a spectral vector 204. Thespectral vector contains at least some number of spectral measurementsof the energy upwelling from that pixel. As indicated in the enlargedsection 206, of row X′ the spectral vector 204A of the initial pixel202A may well be different from the spectral vectors 204B˜204 n forpixels 202B˜202 n.

As is also shown in FIG. 2, a basis vector (BV) set 208 is provided, andis shown to have an initial basis vector 210. In contrast to thelimitations of the principal component analysis (PCA) methods discussedabove, IDRS 100 permits control of the error levels and/or fidelity ofindividual spectra, and as such is highly advantageous when thedetection problem is for spectrally unique objects that sparselypopulate the scene. In addition, the present invention as embodied inIDRS 100, derives basis vectors using no a priori knowledge. The basisvectors span the background materials and (statistically sparse) targetsin the scene. The basis vectors may be endmembers (e.g. pixel spectra)or may have no physical interpretation. The reduced dimensionality datacan control maximum error on each pixel. The number of basis vectors isminimized for a given maximum error level on a per pixel basis.User/Analyst selected spectra can be directly added to the basis vectorset. Spectra collected or measured a priori can be added to the basisvector set. The reduced dimensionality scene can be processed using allhyperspectral (vector-type) processing. The computation of the reduceddimensionality scene is faster than prior art techniques with comparablelevels of reduction. The determination and/or establishment of the BVset 208 and at least the initial basis vector 210 is further discussedbelow.

The method of hyperspectral image dimension reduction 300 is presentedat a high level in FIG. 3. It is understood and appreciated that themethod need not be performed in the order herein described, but ratheris presented for ease of discussion and illustration in accordance withat least one embodiment.

Moreover, in at least one embodiment, a hyperspectral image is provided,block 302. A BV set 208 is developed with respect to the hyperspectralimage, block 304. Then, with the BV set established, each pixel isdecomposed with the BV set to provide a plurality of alpha vectors,block 306. These alpha vectors are provided as the reduced data set ofthe output scene, block 308.

FIG. 4 is a high level block diagram of the system architecture of theIDRS 100, in accordance with at least one embodiment. IDRS 100 may beimplemented on a computer having typical components, such as aprocessor, memory, storage devices, and input and output devices. Duringoperation, IDRS 100 may be maintained in active memory for enhancedspeed and efficiency. In addition, in at least one embodiment, IDRS 100may be operated on a computer network and may utilize distributedresources.

As shown in FIG. 4, for at least one embodiment, IDRS 100 includes aninterface 400, a pool 402, a spectrum reader 404, a decomposer 406 andan evaluator 408. The interface is operable to receive information suchas a hyperspectral image, a user provided basis vector, and/or otherinformation such as for example, but not limited to, whether to performthe dimension reduction as a lossy or lossless operation. The interfacemay receive information directly from a user through an associated inputdevice, or directly from a system component, such as the detectors 116shown in FIG. 1.

With respect to the issue of image reduction as a lossless operation, itis understood that the resulting data may not qualify in general termsas compressed data, for the additional components added to thecompressed element may in fact result in a data set that issubstantially the same size as the original data set. However, it ispossible for the compressed portion of the data to be sent, processed orotherwise utilized and to deliver other data elements at a later time.Moreover, it is possible to utilize a compressed lossy version of thedata for immediate transfer to the ground, and still acquire theadditional data elements so as to achieve lossless version at a laterdate. For these possible benefits, this option is intended to beprovided to users of the system.

The pool 402 is in communication with the interface 400 and is operableto receive basis vectors from the interface, if provided by a user orbuilt in as a default. More generally, the pool 402 is operable toprovide at least one basis vector, and more commonly a set of basisvectors for use in dimensionally reducing the hyperspectral image. Thespectrum reader 404 is operable to read and provide the spectral vectorfor each pixel of the hyperspectral image.

The decomposer 406 is coupled to both the pool 402, so as to receive theBV set, and the spectrum reader 404, so as to receive the spectralvector of a given pixel. The decomposer 406 is operable to decompose thespectral vector with the BV set and provide a result. In at least oneembodiment the decomposer 406 is an unmixer. The evaluator 408 is incommunication with at least the pool 402 and the decomposer 406. In afirst instance, the evaluator is operable to compare the result, e.g.,the magnitude of a residual vector, with a threshold. If the magnitudeof the residual vector is over a threshold value then the evaluator 408will direct the spectral vector be added to the pool 402, and morespecifically the BV set. In a second instance, the evaluator 408 isoperable to provide an alpha vector, a residual vector, and/or themagnitude of the residual vector for each pixel.

It is of course understood and appreciated that the components/elementsof the IDRS 100 as identified above may be implemented withsubcomponents of otherwise further subdivided. Each element has beenillustrated as an identifiable element for ease of discussion andillustration. In addition, as indicated above, in at least oneembodiment, IDRS 100 is implemented in a computer, environment, whereinthe interface 400, the pool 402, the spectrum reader 404, the decomposer406 and the evaluator 408 are implemented as routines, and/or objectsrendered in a variety of different forms of code and instructions as maybe preferred for different computer systems and environments.

FIG. 5 provides a more specific block diagram of the method presented inFIG. 3. More specifically, FIG. 5 provides greater detail for theoperations of developing the BV set, block 304, and generating the alphavectors, block 306. Again it is understood and appreciated that theorder of the steps herein described are provided for ease ofillustration and discussion and not by way of limitation.

More specifically, as shown in FIG. 5, in at least one embodiment theprocess of developing the BV set commences with analysis of whether aseed basis vector has been provided, decision 500. Stated simply, thebasis vector set provides a collection of spectral vectors by which thespectral vectors of all pixels within the hyperspectral image areevaluated. Whereas in at least one embodiment, the evaluation andcomparison is based entirely upon data already present in thehyperspectral image itself, in at least one embodiment at least onebasis vector is provided by an operator or is read from a default file.Such a basis vector may, for example, correspond to the spectral vectorof a particular material known to exist or expected not to exist, withinthe geographic area from which the hyperspectral image was formed.

Where one or more seed vectors are provided as initial basis vectors,the seed basis vector(s) is received and added to the basis vector set210, block 502. To develop the reset of the BV set, an initial pixel (P)is selected, block 504. Generally, and in at least one embodiment, theinitial pixel is selected to be the first pixel in the top left orbottom left corner, the pixels to be compared in an incremental steppattern across each row X.

Where a seed vector is not provided, decision 500, an initial pixel isimmediately selected, block 506. The spectral vector for this initialpixel is then read by the spectrum reader and this initial spectralvector is used as the initial basis vector for the further developmentof the BV set, block 508. In at least one embodiment, the selection ofthe initial pixel to determine the initial basis vector is a randomselection. In at least one alternative embodiment, the initial pixel isselected to be either the top left or bottom left corner pixel.

Moreover, if a seed basis vector(s) is provided then it is treated as ifit was the determined basis vector from an initial pixel and the processimmediately commences with unmixing and testing as discussed below. If aseed basis vector is not provided, then a pixel is selected and itsspectral vector is adopted as the initial basis vector, and then theprocess commences with unmixing and testing as set forth below.

With an initial basis vector so determined, the process increments toselect the next pixel (P), block 510. Generally, and in at least oneembodiment, it is understood and appreciated that the pixels will beincremented through in a left to right iteration for each row X.

The process now continues with the reading of the spectral vector ofpixel P, block 512. The spectral vector of the selected pixel is thendecomposed with the members of the developing BV set. In at least oneembodiment the decomposition is performed by unmixing the spectralvector with the members of the BV set.

Generally stated, unmixing is understood and appreciated to be, inessence, an algebraic approach to decomposing the spectral vector intoits component parts. For example, a spectral vector from a given pixelmay comprise the spectrum elements indicative of grass, aluminum andsand, and thus be a composite spectral vector. When this vector isunmixed with vectors representative of aluminum and sand, the spectralvector of grass will be revealed. The unmixing process provides alphacoefficients representative of the abundance of the basis vectors andwill provide a residual vector. The vector that is unmixed is analgebraic combination of the basis vectors, weighted in some fashion bythe alpha coefficients plus the residual vector.

In at least one embodiment, the magnitude of the residual vector is thendetermined, and is understood and appreciated to be derived from thesquare root of the dot product of the residual vector with itself,otherwise known as Euclidean distance provided by the equation:

∥x∥:=√{square root over (x ₁ ² + . . . +x _(n) ²)}.

where x=[x₁, x₂, . . . , x_(n)].

The magnitude of the residual vector is then evaluated with a thresholdvalue, decision 516. In at least one embodiment the threshold value isprovided by an operator. The threshold value may also be apre-determined value that is hard coded into the method. If themagnitude is over the threshold, decision 516, the spectral vector isadded to the BV set, block 518. In some embodiments, there may be apre-determined maximum number of members of the BV set. When the totalnumber of members is below this pre-determined maximum there is ofcourse no issue and the new spectral vector is simply added to the BVset, decision 520.

If, however, the maximum number of members has been reached, the membersof the BV set are optimized, block 522. Various optimization methods maybe employed and in general the type of optimization strategy adopted isdetermined by an operator. These methods may include, but are notlimited to, optimization to selection of the maximum spectral vectors(e.g., the spectral vectors having the greatest magnitudes),optimization to select the greatest range between the spectral vectors,optimization to select the spectral vectors closest to a spectral vectormean, optimization to select the spectral vectors closest to a spectralmedian.

In at least one embodiment, the residual magnitude is stored when abasis vector is added to the BV set. When the maximum number for the BVset is reached, the magnitude of a new residual must be larger than thesmallest residual computed from the existing members of the BV set. Ifit is not, the new basis vector is not added. If it is, then the newbasis vector is added and the basis vector that previously had thesmallest residual is removed.

As indicated above, in at least one embodiment the user is permitted toprovide seed basic vectors. In at least one such embodiment where theuser has also set a maximum number for the BV set, the seed basisvector(s) are treated as special and maintained as core elements of theBV set. In one embodiment, this is achieved by tagging the seed vectorssuch that they are not considered in the elimination evaluation process.Moreover, in at least one embodiment the maximum number of BV setmembers does not include the seed basis vector(s). In an alternativeembodiment, the maximum number of BV set members may indeed include theseed basis vector(s), but they are simply not included in the evaluationfor removal process. As such, key factors that are indicated by the seedvector(s) are maintained within the BV set.

Having resolved the question of whether to add the residual vector tothe BV set, and whether to optimize the BV set, the method continues toselect the next pixel, decision 524. If indeed more pixels remain thenthe method will inclement to the next pixel, block 526. It should beunderstood and appreciated that, although the incremental process isintended to be orderly, the type of incrementing—left to right, right toleft, top to bottom, bottom to top, or random—is largely immaterial. Thekey element is that each pixel of the hyperspectral image is beingevaluated so as to determine an appropriate set of basis vectors thatare, in at least one embodiment, inherently tied to hyperspectral imageitself.

With the BV set so established, the method now continues with thegeneration of the alpha vectors, block 306. As in the development of theBV set, the generation of the alpha vectors begins with the selection ofan initial pixel, block 528. The spectral vector is then read from theselected pixel, block 530. The spectral vector is then decomposed withthe BV set, block 532. In at least one embodiment the decomposition isperformed as unmixing, as discussed above. It is to be realized that foreach member of the BV set there will be a corresponding value, anunmixing coefficient. There will also be a residual vector.

For the given pixel, the unmixing coefficients represent an alpha vectorfor the given pixel. It is further understood and appreciated that therelative magnitude of dimension reduction is approximately thedifference between the number of elements in a spectral vectororiginally present in the hyperspectral image and the number of membersin the applied BV set. For example, if there were originally one-hundredspectral bands present in the hyperspectral image, and twenty basisvectors in the BV set, the dimension reduction represented by the alphavectors would be about eighty or a factor of five.

The process is continued for each pixel, as indicated by the query todetermine the presence of more pixels, decision 536, and the incrementto the next pixel, block 538 and the return to read the spectral vectorfor the selected pixel, block 530. Moreover, all of the pixels in thehyperspectral image scene are unmixed with the BV set so as to providevectors having g coefficients (corresponding to the number of members inthe BV set) and a residual vector for each X-Y location.

When the generation of alpha vectors has been accomplished for allpixels of the hyperspectral image, the resulting set of alpha vectors isprovided as the output scene, namely a dimensionally reduced hypercube.Because of the application of the BV set, which is derived from thespectral vectors of each pixel, to each pixel, the resulting informationof each alpha vector has approximately the same information content asthe original pixel. More specifically, the reduced alpha vector is stillrepresentative of the composite spectral frequencies present in thepixel, such a combination of grass, sand and metal as present in theoriginal spectral vectors is still represented in the alpha vector.Furthermore, and quite advantageously, the dimensionally reduced imageprovided by the alpha pixels is suitable for processing withoutregeneration of the original image. Further, the maximum loss ofinformation on any pixel is minimized compared to any otherdimensionality reduction process. This is crucial when the image isprocessed for subtle changes, the presence of small targets oranomalies.

Decompression of the output image can be accomplished with the use ofthe BV set. As such, in at least one embodiment the BV set is providedas output with the dimension reduced image. The residual vectors, ifleft untreated are understood to be of the same size as the originaldata set. Calculating the magnitude for each residual vector greatlyreduces the volume of the data once again.

Indeed, in at least one embodiment the magnitude of each residual vectoris also determined and provided as output in addition to dimensionallyreduced image represented by the alpha vectors. Moreover, it isunderstood and appreciated that in varying embodiments, the method 300may provide: the alpha vectors, the magnitude of the residual vectors,the BV set, and combinations thereof. As such, it is understood that themethod is adaptable to provide compression and dimension reductionoptions that range from lossy to lossless.

FIG. 6 is a high level block diagram of an exemplary computer system 600as may be used to provide IDRS 100, and/or embodiments of method 300.Computer system 600 has a case 602, enclosing a main board 604. The mainboard has a system bus 606, connection ports 608, a processing unit,such as Central Processing Unit (CPU) 610, and a memory storage device,such as main memory 612, hard drive 614, and CD/DVD Rom drive 616.

Memory bus 618 couples main memory 612 to CPU 610. A system bus 606couples hard drive 614, CD/DVD Rom drive 616, and connection ports 608to CPU 610. Multiple input devices may be provided, such as for examplea mouse 620 and keyboard 622. Multiple output devices may also beprovided, such as for example a video monitor 624 and a printer (notshown).

Computer system 600 may be a commercially available system, such as adesktop workstation unit provided by IBM, Dell Computers, Gateway,Apple, Sun Micro Systems, or other computer system provider. Computersystem 600 may also be a networked computer system, wherein memorystorage components such as hard drive 614, additional CPUs 610 andoutput devices such as printers are provided by physically separatecomputer systems commonly tied together in the network. Those skilled inthe art will understand and appreciate the physical composition ofcomponents and component interconnections comprising computer system600, and select a computer system 600 suitable for the schedules to beestablished and maintained.

When computer system 600 is activated, preferably an operating system626 will load into main memory 612 as part of the boot strap startupsequence and ready the computer system 600 for operation. At thesimplest level, and in the most general sense, the tasks of an operatingsystem fall into specific categories—process management, devicemanagement (including application and user interface management) andmemory management.

In such a computer system 600, the CPU 610 is operable to perform one ormore of the scheduling embodiments described above. Those skilled in theart will understand that a computer-readable medium 628 on which is acomputer program 630 for adding activities to a schedule may be providedto the computer system 600. The form of the medium 628 and language ofthe program 630 are understood to be appropriate for computer system600. Utilizing the memory stores, such as for example one or more harddrives 614 and main system memory 612, the operable CPU 602 will readthe instructions provided by the computer program 630 and operate toperform the method of hyperspectral image dimension reduction 300 asdescribed above.

Changes may be made in the above methods, systems and structures withoutdeparting from the scope hereof. It should thus be noted that the mattercontained in the above description and/or shown in the accompanyingdrawings should be interpreted as illustrative and not in a limitingsense. The following claims are intended to cover all generic andspecific features described herein, as well as all statements of thescope of the present method, system and structure, which, as a matter oflanguage, might be said to fall therebetween.

1. A method of hyperspectral image dimension reduction, comprising:receiving a hyperspectral image having a plurality of pixels;establishing a basis vector (BV) set; for each of the plurality ofpixels: reading a spectral vector from the pixel; decomposing thespectral vector and the BV set to provide a reduced dimension vector forthe pixel; and providing a dimension reduced image consisting of thereduced dimension vectors for each pixel.
 2. The method of claim 1,wherein the decomposing is unmixing.
 3. The method of claim 1, whereinthe BV set is provided with the dimension reduced image.
 4. The methodof claim 1, wherein decomposing the spectral vector and the BV setfurther includes providing a residual vector for the pixel.
 5. Themethod of claim 4, wherein at least the magnitude of the residual vectoris also provided with the dimension reduced image.
 6. The method ofclaim 5, wherein the basis vector set is provided with the dimensionreduced image.
 7. The method of claim 1, wherein the dimension reductionis performed as a lossy process.
 8. The method of claim 1, wherein thedimension reduction is performed as a lossless process.
 9. The method ofclaim 1, wherein the dimension reduced image includes an alpha vectorand a residual vector for each pixel.
 10. The method of claim 1, whereinestablishing a basis vector (BV) set includes receiving at least onepre-determined basis vector.
 11. The method of claim 1, whereinestablishing a basis vector (BV) set includes: selecting an initialpixel; reading the spectral vector for the initial pixel, the spectralvector set as an initial basis vector (BV) of the set; and for each ofthe remaining plurality of pixels: reading a spectral vector from thepixel; unmixing the spectral vector with the BV set to determine aresidual vector; and in response to a magnitude of the residual vectorbeing greater than a threshold, adding the spectral vector to the BVset.
 12. The method of claim 11, wherein the threshold is provided by anoperator along with the hyperspectral image.
 13. The method of claim 11,wherein the BV set has a pre-determined maximum number of members, andin response to an attempt to exceed the maximum number, optimizing theset to maintain the maximum number of spectral vectors.
 14. The methodof claim 13, wherein the pre-determined maximum number of members isprovided by an operator along with the hyperspectral image.
 15. Themethod of claim 13, wherein the BV set is optimized to maintain thepre-determined maximum number of members.
 16. The method of claim 15,wherein for optimization, when the maximum number for the BV set isreaches, a residual magnitude of a new candidate vector is compared toresidual magnitude values of BV set members, the new candidate replacinga BV set member in response to the residual magnitude of the newcandidate being larger than a smallest residual computed from theexisting BV set member.
 17. A method of hyperspectral image dimensionreduction, comprising: receiving a hyperspectral image having aplurality of pixels; establishing a basis vector (BV) set by: selectingan initial pixel; reading the spectral vector for the initial pixel, thespectral vector set as an initial basis vector (BV) of the set; and foreach of the remaining plurality of pixels: reading a spectral vectorfrom the pixel; unmixing the spectral vector with the BV set todetermine a residual vector; and in response to a magnitude of theresidual vector being greater than a threshold, adding the spectralvector to the BV set; for each of the plurality of pixels: reading aspectral vector from the pixel; unmixing the spectral vector with the BVset to provide an alpha vector and a residual vector for the pixel; andproviding a dimension reduced image consisting of at least the alphavector for each pixel.
 18. The method of claim 17, wherein the dimensionreduced image consists of the alpha vector and a magnitude of theresidual vector for each pixel and the basis vector set.
 19. The methodof claim 18, wherein the BV set is provided with the dimension reducedimage.
 20. The method of claim 17, wherein establishing a basis vector(BV) set includes receiving at least one pre-determined basis vectorfrom an operator.
 21. The method of claim 17, wherein the threshold isprovided by an operator along with the hyperspectral image.
 22. Themethod of claim 17, wherein the BV set has a pre-determined maximumnumber of members, and in response to an attempt to exceed the maximumnumber, optimizing the set to maintain the maximum number of spectralvectors.
 23. The method of claim 22, wherein for optimization, when themaximum number for the BV set is reaches, a residual magnitude of a newcandidate vector is compared to residual magnitude values of BV setmembers, the new candidate replacing a BV set member in response to theresidual magnitude of the new candidate being larger than a smallestresidual computed from the existing BV set member.
 24. The method ofclaim 17, wherein the dimension reduction is performed as a lossyprocess.
 25. The method of claim 17, wherein the dimension reduction isperformed as a lossless process.
 26. A system for hyperspectral imagedimension reduction, comprising: an interface operable to receive atleast a hyperspectral image having a plurality of pixels; a spectrumreader operable to read a spectral vector for each pixel; a pooloperable to provide a basis vector (BV) set having at least one basisvector; a decomposer operable to decompose the spectral vector of eachpixel with at least one basis vector and provided a result; and anevaluator in connection with the pool and decomposer and operable in atleast a first instance to compare the result to a threshold value, andoperable in at least a second instance to provide an alpha vector and aresidual vector for each pixel.
 27. The system of claim 26, wherein theinterface is operable to receive the threshold value from an operator.28. The system of claim 26, wherein the interface is operable to receivea limit for the pool size from an operator.
 29. The system of claim 26,wherein the decomposer is an unmixer.
 30. The system of claim 26,wherein for the first instance, the evaluator compares the result to thethreshold value and in response to the result being greater than thethreshold value, the evaluator adding the spectral vector to the pool.31. The system of claim 26, wherein the system is operable to provide adimensionally reduced hypercube of alpha vectors.
 32. The system ofclaim 26, wherein the dimension reduction is performed as a lossyprocess.
 33. The system of claim 26, wherein the dimension reduction isperformed as a lossless process.